Main

In your final repo, there should be an R markdown file that organizes all computational steps for evaluating your proposed Facial Expression Recognition framework.

This file is currently a template for running evaluation experiments. You should update it according to your codes but following precisely the same structure.

if(!require("EBImage")){
  install.packages("BiocManager")
  BiocManager::install("EBImage")
}
if(!require("R.matlab")){
  install.packages("R.matlab")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("dplyr")){
  install.packages("dplyr")
}
if(!require("readxl")){
  install.packages("readxl")
}

if(!require("ggplot2")){
  install.packages("ggplot2")
}

if(!require("caret")){
  install.packages("caret")
}

if(!require("glmnet")){
  install.packages("glmnet")
}

if(!require("WeightedROC")){
  install.packages("WeightedROC")
}

if(!require("gbm")){
  install.packages("gbm")
}

if(!require("xgboost")){
  install.packages("xgboost")
}

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if(!require("caret")){
  install.packages("caret")
}

# Install Miniconda (https://docs.conda.io/en/latest/miniconda.html)
if(!require("keras")){
  install.packages("keras")
}

if(!require("tensorflow")){
  install.packages("tensorflow")
  install_tensorflow()
}

use_condaenv("r-tensorflow")
library(keras)
library(tensorflow)

library(R.matlab)
library(readxl)
library(dplyr)
library(EBImage)
library(ggplot2)
library(caret)
library(glmnet)
library(WeightedROC)
library(gbm)
require(xgboost)
library(caret)

Step 0 set work directories

set.seed(2020)
# setwd("~/Project3-FacialEmotionRecognition/doc")
# here replace it with your own path or manually set it in RStudio to where this rmd file is located. 
# use relative path for reproducibility

Provide directories for training images. Training images and Training fiducial points will be in different subfolders.

train_dir <- "../data/train_set/" # This will be modified for different data sets.
train_image_dir <- paste(train_dir, "images/", sep="")
train_pt_dir <- paste(train_dir,  "points/", sep="")
train_label_path <- paste(train_dir, "label.csv", sep="") 

Step 1: set up controls for evaluation experiments.

In this chunk, we have a set of controls for the evaluation experiments.

• (T/F) cross-validation on the training set
• (T/F) reweighting the samples for training set
• (number) K, the number of CV folds
• (T/F) process features for training set
• (T/F) run evaluation on an independent test set
• (T/F) process features for test set

# run.cv <- TRUE # run cross-validation on the training set
# sample.reweight <- TRUE # run sample reweighting in model training
# K <- 5  # number of CV folds
# run.feature.train <- TRUE # process features for training set
# run.test <- TRUE # run evaluation on an independent test set
# run.feature.test <- TRUE # process features for test set

sample.reweight <- TRUE # run sample reweighting in model training
K <- 5  # number of CV folds
run.feature.train <- TRUE # process features for training set
run.feature.test <- TRUE # process features for test set

run.cv_gbm <- TRUE # run GBM cross-validation on the training set
run.test_gbm <- TRUE # run GBM evaluation on an independent test set

run.cv_dnn <- TRUE # run DNN cross-validation on the training set
run.test_dnn <- TRUE # run DNN evaluation on an independent test set

Using cross-validation or independent test set evaluation, we compare the performance of models with different specifications. In this part, we tune parameter n.trees and shrinkage for GBM.

# GBM parameters
n.trees <- c(500, 1000, 1500)
shrinkage <- c(0.01, 0.05, 0.1)

# DNN parameters
lrs <- c(0.0002, 0.0005, 0.001)

Step 2: import data and train-test split

#train-test split
info <- read.csv(train_label_path)
n <- nrow(info)
n_train <- round(n*(4/5), 0)
train_idx <- sample(info$Index, n_train, replace = F)
test_idx <- setdiff(info$Index, train_idx)

If you choose to extract features from images, such as using Gabor filter, R memory will exhaust all images are read together. The solution is to repeat reading a smaller batch(e.g 100) and process them.

n_files <- length(list.files(train_image_dir))

image_list <- list()
for(i in 1:100){
   image_list[[i]] <- readImage(paste0(train_image_dir, sprintf("%04d", i), ".jpg"))
}

Fiducial points are stored in matlab format. In this step, we read them and store them in a list.

#function to read fiducial points
#input: index
#output: matrix of fiducial points corresponding to the index
readMat.matrix <- function(index){
     return(round(readMat(paste0(train_pt_dir, sprintf("%04d", index), ".mat"))[[1]],0))
}

#load fiducial points
fiducial_pt_list <- lapply(1:n_files, readMat.matrix)

strings not representable in native encoding will be translated to UTF-8

save(fiducial_pt_list, file="../output/fiducial_pt_list.RData")

Step 3: construct features and responses

• The follow plots show how pairwise distance between fiducial points can work as feature for facial emotion recognition.

  • In the first column, 78 fiducials points of each emotion are marked in order.
  • In the second column distributions of vertical distance between right pupil(1) and right brow peak(21) are shown in histograms. For example, the distance of an angry face tends to be shorter than that of a surprised face.
  • The third column is the distributions of vertical distances between right mouth corner(50) and the midpoint of the upper lip(52). For example, the distance of an happy face tends to be shorter than that of a sad face.

Figure1

feature.R should be the wrapper for all your feature engineering functions and options. The function feature( ) should have options that correspond to different scenarios for your project and produces an R object that contains features and responses that are required by all the models you are going to evaluate later.

• feature.R
• Input: list of images or fiducial point
• Output: an RData file that contains extracted features and corresponding responses

source("../lib/feature.R")
tm_feature_train <- NA
if(run.feature.train){
  tm_feature_train <- system.time(dat_train <- feature(fiducial_pt_list, train_idx))
  save(dat_train, file="../output/feature_train.RData")
}else{
  load(file="../output/feature_train.RData")
}

tm_feature_test <- NA
if(run.feature.test){
  tm_feature_test <- system.time(dat_test <- feature(fiducial_pt_list, test_idx))
  save(dat_test, file="../output/feature_test.RData")
}else{
  load(file="../output/feature_test.RData")
}

GBM (Baseline Model)

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

• train.R
  • Input: a data frame containing features and labels and a parameter list.
  • Output:a trained model
• test.R
  • Input: the fitted classification model using training data and processed features from testing images
  • Input: an R object that contains a trained classifier.
  • Output: training model specification
• In this part, we use GBM (baseline model) to do classification.

source("../lib/train_gbm.R") 
source("../lib/test_gbm.R")

Model selection with cross-validation

• Do model selection by choosing among different values of training model parameters.

source("../lib/cross_validation_gbm.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)

if(run.cv_gbm){
  res_cv_gbm <- matrix(0, nrow = length(n.trees) * length(shrinkage), ncol = 6)
  count = 0
  for(i in 1:length(n.trees)){
    for(j in 1:length(shrinkage)){
      count = count + 1
      cat("n.trees =", n.trees[i], "\n")
      cat("shrinkage =", shrinkage[j], "\n")
      
      res_cv <- cv.function_gbm(features = feature_train, labels = label_train, K,
                              n.trees[i], shrinkage[j], reweight = sample.reweight)
      
      res_cv_gbm[count,] <- c(n.trees[i], shrinkage[j], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
    }
  }
  
  colnames(res_cv_gbm) <- c("n.trees","shrinkage","mean_error", "sd_error", "mean_AUC", "sd_AUC")
  save(res_cv_gbm, file="../output/res_cv_gbm.RData")
}else{
  load("../output/res_cv_gbm.RData")
}

Visualize cross-validation results.

res_cv_gbm <- as.data.frame(res_cv_gbm) 

if(run.cv_gbm){
  p1 <- res_cv_gbm %>% 
    ggplot(aes(x = n.trees, y = mean_error,
               ymin = mean_error - sd_error, ymax = mean_error + sd_error)) + 
    geom_crossbar() +
    facet_wrap(~shrinkage)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  p2 <- res_cv_gbm %>% 
    ggplot(aes(x = n.trees, y = mean_AUC,
               ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) + 
    geom_crossbar() +
    facet_wrap(~shrinkage)+
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  print(p1)
  print(p2)
}

• Choose the “best” parameter value

# par_n.trees_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 1])
# par_shrinkage_best <- as.numeric(res_cv_gbm[which.min(res_cv_gbm$mean_error), 2])

par_n.trees_best <- 500
par_shrinkage_best <- 0.05

• Train the model with the entire training set using the selected model (model parameter) via cross-validation.

feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

tm_train <- NA
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = weight_train, par_n.trees_best, par_shrinkage_best))
} else {
  tm_train <- system.time(fit_train <- train_gbm(feature_train, label_train, w = NULL, par_n.trees_best, par_shrinkage_best))
}
save(fit_train, file="../output/fit_train_gbm.RData")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_gbm){
  load(file="../output/fit_train_gbm.RData")
  tm_test <- system.time({prob_pred <- test_gbm(fit_train, feature_test, par_n.trees_best, pred.type = 'response');})
}

• evaluation

## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)

weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

label_pred <- ifelse(prob_pred > 0.5, 1, 0)
label_test <- ifelse(label_test == 2, 1, 0)

accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)
tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)


cat("The accuracy of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", accu*100, "%.\n")

The accuracy of model: GBM with n.trees = 500 and shrinkage = 0.05 is 74.02552 %.

cat("The AUC of model:", "GBM with n.trees =" , par_n.trees_best, "and shrinkage =", par_shrinkage_best, "is", auc, ".\n")

The AUC of model: GBM with n.trees = 500 and shrinkage = 0.05 is 0.7922946 .

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[3], "s \n")

Time for constructing training features= 0.89 s 

cat("Time for constructing testing features=", tm_feature_test[3], "s \n")

Time for constructing testing features= 0.13 s 

cat("Time for training model=", tm_train[3], "s \n") 

Time for training model= 75.21 s 

cat("Time for testing model=", tm_test[3], "s \n")

Time for testing model= 0.12 s 

DNN (Dense Neural Network) (Advanced Model)

Step 4: Train a classification model with training features and responses

Call the train model and test model from library.

train.R and test.R should be wrappers for all your model training steps and your classification/prediction steps.

• train.R
  • Input: a data frame containing features and labels and a parameter list.
  • Output:a trained model
• test.R
  • Input: the fitted classification model using training data and processed features from testing images
  • Input: an R object that contains a trained classifier.
  • Output: training model specification
• In this part, we use DNN (advanced model) to do classification.

source("../lib/train_dnn.R") 
source("../lib/test_dnn.R")

Model selection with cross-validation

• Do model selection by choosing among different values of training model parameters.

source("../lib/cross_validation_dnn.R")
feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)

if(run.cv_dnn){
  # res_cv_dnn <- matrix(0, 1, ncol = 4)
  res_cv_dnn <- matrix(0, nrow = length(lrs), ncol = 6)
  count = 0
  for(i in 1:length(lrs)){
    count = count + 1
    cat("lr =", lrs[i], "\n")
    
    res_cv <- cv.function_dnn(features = feature_train, labels = label_train, K,
                            reweight = sample.reweight, lr = lrs[i])
    
    res_cv_dnn[count,] <- c(lr[i], res_cv[1], res_cv[2], res_cv[3], res_cv[4])
  }
  
  res_cv <- cv.function_dnn(features = feature_train, labels = label_train, K,
                          reweight = sample.reweight, lr = lrs[i])

  colnames(res_cv_dnn) <- c("lr", "mean_error", "sd_error", "mean_AUC", "sd_AUC")
  save(res_cv_dnn, file="../output/res_cv_dnn.RData")
}else{
  load("../output/res_cv_dnn.RData")
}

lr = 2e-04 
Epoch 1/100

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Epoch 2/100

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Epoch 3/100

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Epoch 4/100

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Epoch 5/100

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Epoch 6/100

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39/39 [==============================] - 1s 32ms/step - loss: 1.6426 - accuracy: 0.6557

39/39 [==============================] - 1s 38ms/step - loss: 1.6426 - accuracy: 0.6557 - val_loss: 1.6102 - val_accuracy: 0.5979
Epoch 7/100

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39/39 [==============================] - 1s 32ms/step - loss: 1.6204 - accuracy: 0.6714

39/39 [==============================] - 2s 40ms/step - loss: 1.6204 - accuracy: 0.6714 - val_loss: 1.6317 - val_accuracy: 0.5583
Epoch 8/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.6150 - accuracy: 0.6734

39/39 [==============================] - 2s 43ms/step - loss: 1.6150 - accuracy: 0.6734 - val_loss: 1.5954 - val_accuracy: 0.6042
Epoch 9/100

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39/39 [==============================] - 1s 35ms/step - loss: 1.5900 - accuracy: 0.6990

39/39 [==============================] - 2s 42ms/step - loss: 1.5900 - accuracy: 0.6990 - val_loss: 1.6163 - val_accuracy: 0.5646
Epoch 10/100

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39/39 [==============================] - 1s 35ms/step - loss: 1.5618 - accuracy: 0.6948

39/39 [==============================] - 2s 42ms/step - loss: 1.5618 - accuracy: 0.6948 - val_loss: 1.5994 - val_accuracy: 0.5708
Epoch 11/100

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39/39 [==============================] - 1s 35ms/step - loss: 1.5327 - accuracy: 0.7156

39/39 [==============================] - 2s 42ms/step - loss: 1.5327 - accuracy: 0.7156 - val_loss: 1.5647 - val_accuracy: 0.6604
Epoch 12/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.5380 - accuracy: 0.7130

39/39 [==============================] - 2s 42ms/step - loss: 1.5380 - accuracy: 0.7130 - val_loss: 1.5329 - val_accuracy: 0.6687
Epoch 13/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.5117 - accuracy: 0.7286

39/39 [==============================] - 2s 44ms/step - loss: 1.5117 - accuracy: 0.7286 - val_loss: 1.5546 - val_accuracy: 0.6396
Epoch 14/100

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39/39 [==============================] - 1s 34ms/step - loss: 1.5016 - accuracy: 0.7203

39/39 [==============================] - 2s 41ms/step - loss: 1.5016 - accuracy: 0.7203 - val_loss: 1.5300 - val_accuracy: 0.6542
Epoch 15/100

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39/39 [==============================] - 1s 35ms/step - loss: 1.4969 - accuracy: 0.7385

39/39 [==============================] - 2s 42ms/step - loss: 1.4969 - accuracy: 0.7385 - val_loss: 1.5181 - val_accuracy: 0.6562
Epoch 16/100

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39/39 [==============================] - 1s 35ms/step - loss: 1.4841 - accuracy: 0.7250

39/39 [==============================] - 2s 42ms/step - loss: 1.4841 - accuracy: 0.7250 - val_loss: 1.5504 - val_accuracy: 0.6458
Epoch 17/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.4632 - accuracy: 0.7323

39/39 [==============================] - 2s 43ms/step - loss: 1.4632 - accuracy: 0.7323 - val_loss: 1.5230 - val_accuracy: 0.6771
Epoch 18/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.4656 - accuracy: 0.7370

39/39 [==============================] - 2s 43ms/step - loss: 1.4656 - accuracy: 0.7370 - val_loss: 1.5146 - val_accuracy: 0.6687
Epoch 19/100

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39/39 [==============================] - 1s 34ms/step - loss: 1.4239 - accuracy: 0.7615

39/39 [==============================] - 2s 41ms/step - loss: 1.4239 - accuracy: 0.7615 - val_loss: 1.5069 - val_accuracy: 0.6896
Epoch 20/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.4257 - accuracy: 0.7573

39/39 [==============================] - 2s 42ms/step - loss: 1.4257 - accuracy: 0.7573 - val_loss: 1.4749 - val_accuracy: 0.6958
Epoch 21/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.3930 - accuracy: 0.7703

39/39 [==============================] - 2s 42ms/step - loss: 1.3930 - accuracy: 0.7703 - val_loss: 1.5150 - val_accuracy: 0.6750
Epoch 22/100

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39/39 [==============================] - 1s 35ms/step - loss: 1.3996 - accuracy: 0.7698

39/39 [==============================] - 2s 42ms/step - loss: 1.3996 - accuracy: 0.7698 - val_loss: 1.4835 - val_accuracy: 0.7063
Epoch 23/100

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39/39 [==============================] - 1s 34ms/step - loss: 1.3859 - accuracy: 0.7760

39/39 [==============================] - 2s 42ms/step - loss: 1.3859 - accuracy: 0.7760 - val_loss: 1.4610 - val_accuracy: 0.7125
Epoch 24/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.3823 - accuracy: 0.7703

39/39 [==============================] - 2s 44ms/step - loss: 1.3823 - accuracy: 0.7703 - val_loss: 1.5027 - val_accuracy: 0.6854
Epoch 25/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.3701 - accuracy: 0.7609

39/39 [==============================] - 2s 44ms/step - loss: 1.3701 - accuracy: 0.7609 - val_loss: 1.4771 - val_accuracy: 0.7042
Epoch 26/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.3673 - accuracy: 0.7797

39/39 [==============================] - 2s 44ms/step - loss: 1.3673 - accuracy: 0.7797 - val_loss: 1.4553 - val_accuracy: 0.7000
Epoch 27/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.3447 - accuracy: 0.7901

39/39 [==============================] - 2s 44ms/step - loss: 1.3447 - accuracy: 0.7901 - val_loss: 1.4393 - val_accuracy: 0.7479
Epoch 28/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.3390 - accuracy: 0.7839

39/39 [==============================] - 2s 44ms/step - loss: 1.3390 - accuracy: 0.7839 - val_loss: 1.4313 - val_accuracy: 0.7354
Epoch 29/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.3076 - accuracy: 0.8016

39/39 [==============================] - 2s 44ms/step - loss: 1.3076 - accuracy: 0.8016 - val_loss: 1.4467 - val_accuracy: 0.7104
Epoch 30/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.3095 - accuracy: 0.8057

39/39 [==============================] - 2s 43ms/step - loss: 1.3095 - accuracy: 0.8057 - val_loss: 1.4289 - val_accuracy: 0.7375
Epoch 31/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2736 - accuracy: 0.8062

39/39 [==============================] - 2s 43ms/step - loss: 1.2736 - accuracy: 0.8062 - val_loss: 1.4412 - val_accuracy: 0.7625
Epoch 32/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2742 - accuracy: 0.8167

39/39 [==============================] - 2s 44ms/step - loss: 1.2742 - accuracy: 0.8167 - val_loss: 1.4393 - val_accuracy: 0.7500
Epoch 33/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.3000 - accuracy: 0.8057

39/39 [==============================] - 2s 43ms/step - loss: 1.3000 - accuracy: 0.8057 - val_loss: 1.4924 - val_accuracy: 0.7167
Epoch 34/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.2763 - accuracy: 0.8031

39/39 [==============================] - 2s 44ms/step - loss: 1.2763 - accuracy: 0.8031 - val_loss: 1.4614 - val_accuracy: 0.7333
Epoch 35/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2541 - accuracy: 0.8172

39/39 [==============================] - 2s 44ms/step - loss: 1.2541 - accuracy: 0.8172 - val_loss: 1.4424 - val_accuracy: 0.7729
Epoch 36/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2335 - accuracy: 0.8266

39/39 [==============================] - 2s 44ms/step - loss: 1.2335 - accuracy: 0.8266 - val_loss: 1.4674 - val_accuracy: 0.7396
Epoch 37/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2388 - accuracy: 0.8120

39/39 [==============================] - 2s 44ms/step - loss: 1.2388 - accuracy: 0.8120 - val_loss: 1.4244 - val_accuracy: 0.7375
Epoch 38/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2134 - accuracy: 0.8219

39/39 [==============================] - 2s 44ms/step - loss: 1.2134 - accuracy: 0.8219 - val_loss: 1.4763 - val_accuracy: 0.7812
Epoch 39/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2147 - accuracy: 0.8255

39/39 [==============================] - 2s 44ms/step - loss: 1.2147 - accuracy: 0.8255 - val_loss: 1.4473 - val_accuracy: 0.7208
Epoch 40/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.1994 - accuracy: 0.8234

39/39 [==============================] - 2s 45ms/step - loss: 1.1994 - accuracy: 0.8234 - val_loss: 1.4255 - val_accuracy: 0.7667
Epoch 41/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.2046 - accuracy: 0.8146

39/39 [==============================] - 2s 44ms/step - loss: 1.2046 - accuracy: 0.8146 - val_loss: 1.4846 - val_accuracy: 0.7896
Epoch 42/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.1887 - accuracy: 0.8349

39/39 [==============================] - 2s 44ms/step - loss: 1.1887 - accuracy: 0.8349 - val_loss: 1.4326 - val_accuracy: 0.7437
Epoch 43/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.1457 - accuracy: 0.8490

39/39 [==============================] - 2s 44ms/step - loss: 1.1457 - accuracy: 0.8490 - val_loss: 1.4377 - val_accuracy: 0.7896
Epoch 44/100

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39/39 [==============================] - 1s 38ms/step - loss: 1.1503 - accuracy: 0.8422

39/39 [==============================] - 2s 45ms/step - loss: 1.1503 - accuracy: 0.8422 - val_loss: 1.4572 - val_accuracy: 0.7812
Epoch 45/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.1225 - accuracy: 0.8562

39/39 [==============================] - 2s 44ms/step - loss: 1.1225 - accuracy: 0.8562 - val_loss: 1.4362 - val_accuracy: 0.7625
Epoch 46/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.1106 - accuracy: 0.8536

39/39 [==============================] - 2s 44ms/step - loss: 1.1106 - accuracy: 0.8536 - val_loss: 1.4354 - val_accuracy: 0.7875
Epoch 47/100

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39/39 [==============================] - 2s 40ms/step - loss: 1.1343 - accuracy: 0.8464

39/39 [==============================] - 2s 49ms/step - loss: 1.1343 - accuracy: 0.8464 - val_loss: 1.3956 - val_accuracy: 0.7708
Epoch 48/100

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39/39 [==============================] - 1s 35ms/step - loss: 1.1226 - accuracy: 0.8510

39/39 [==============================] - 2s 44ms/step - loss: 1.1226 - accuracy: 0.8510 - val_loss: 1.4011 - val_accuracy: 0.7542
Epoch 49/100

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39/39 [==============================] - 2s 40ms/step - loss: 1.1175 - accuracy: 0.8469

39/39 [==============================] - 2s 49ms/step - loss: 1.1175 - accuracy: 0.8469 - val_loss: 1.3889 - val_accuracy: 0.7604
Epoch 50/100

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39/39 [==============================] - 1s 37ms/step - loss: 1.0896 - accuracy: 0.8495

39/39 [==============================] - 2s 43ms/step - loss: 1.0896 - accuracy: 0.8495 - val_loss: 1.3804 - val_accuracy: 0.7625
Epoch 51/100

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39/39 [==============================] - 2s 40ms/step - loss: 1.0962 - accuracy: 0.8510

39/39 [==============================] - 2s 48ms/step - loss: 1.0962 - accuracy: 0.8510 - val_loss: 1.4388 - val_accuracy: 0.7333
Epoch 52/100

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39/39 [==============================] - 2s 40ms/step - loss: 1.1194 - accuracy: 0.8339

39/39 [==============================] - 2s 49ms/step - loss: 1.1194 - accuracy: 0.8339 - val_loss: 1.3961 - val_accuracy: 0.7792
Epoch 53/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.0942 - accuracy: 0.8391

39/39 [==============================] - 2s 44ms/step - loss: 1.0942 - accuracy: 0.8391 - val_loss: 1.4196 - val_accuracy: 0.7521
Epoch 54/100

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39/39 [==============================] - 2s 40ms/step - loss: 1.0497 - accuracy: 0.8625

39/39 [==============================] - 2s 49ms/step - loss: 1.0497 - accuracy: 0.8625 - val_loss: 1.4607 - val_accuracy: 0.7937
Epoch 55/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.0560 - accuracy: 0.8661

39/39 [==============================] - 2s 44ms/step - loss: 1.0560 - accuracy: 0.8661 - val_loss: 1.4135 - val_accuracy: 0.7917
Epoch 56/100

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39/39 [==============================] - 2s 41ms/step - loss: 1.0484 - accuracy: 0.8604

39/39 [==============================] - 2s 49ms/step - loss: 1.0484 - accuracy: 0.8604 - val_loss: 1.4665 - val_accuracy: 0.7750
Epoch 57/100

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39/39 [==============================] - 1s 36ms/step - loss: 1.0535 - accuracy: 0.8604

39/39 [==============================] - 2s 45ms/step - loss: 1.0535 - accuracy: 0.8604 - val_loss: 1.4029 - val_accuracy: 0.7583
Epoch 58/100

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39/39 [==============================] - 2s 40ms/step - loss: 1.0223 - accuracy: 0.8589

39/39 [==============================] - 2s 49ms/step - loss: 1.0223 - accuracy: 0.8589 - val_loss: 1.4266 - val_accuracy: 0.7729
Epoch 59/100

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39/39 [==============================] - 1s 36ms/step - loss: 0.9953 - accuracy: 0.8693

39/39 [==============================] - 2s 44ms/step - loss: 0.9953 - accuracy: 0.8693 - val_loss: 1.4173 - val_accuracy: 0.7812
Epoch 60/100

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39/39 [==============================] - 2s 40ms/step - loss: 1.0114 - accuracy: 0.8682

39/39 [==============================] - 2s 49ms/step - loss: 1.0114 - accuracy: 0.8682 - val_loss: 1.4970 - val_accuracy: 0.7708
Epoch 61/100

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39/39 [==============================] - 1s 37ms/step - loss: 0.9774 - accuracy: 0.8760

39/39 [==============================] - 2s 43ms/step - loss: 0.9774 - accuracy: 0.8760 - val_loss: 1.4325 - val_accuracy: 0.7583
Epoch 62/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.9871 - accuracy: 0.8734

39/39 [==============================] - 2s 51ms/step - loss: 0.9871 - accuracy: 0.8734 - val_loss: 1.4680 - val_accuracy: 0.7708
Epoch 63/100

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39/39 [==============================] - 1s 36ms/step - loss: 0.9521 - accuracy: 0.8854

39/39 [==============================] - 2s 43ms/step - loss: 0.9521 - accuracy: 0.8854 - val_loss: 1.4232 - val_accuracy: 0.7625
Epoch 64/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.9656 - accuracy: 0.8740

39/39 [==============================] - 2s 49ms/step - loss: 0.9656 - accuracy: 0.8740 - val_loss: 1.4387 - val_accuracy: 0.7688
Epoch 65/100

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39/39 [==============================] - 1s 36ms/step - loss: 0.9735 - accuracy: 0.8823

39/39 [==============================] - 2s 44ms/step - loss: 0.9735 - accuracy: 0.8823 - val_loss: 1.4134 - val_accuracy: 0.7667
Epoch 66/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.9402 - accuracy: 0.8750

39/39 [==============================] - 2s 46ms/step - loss: 0.9402 - accuracy: 0.8750 - val_loss: 1.4612 - val_accuracy: 0.7646
Epoch 67/100

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39/39 [==============================] - 1s 37ms/step - loss: 0.9556 - accuracy: 0.8766

39/39 [==============================] - 2s 46ms/step - loss: 0.9556 - accuracy: 0.8766 - val_loss: 1.4638 - val_accuracy: 0.7312
Epoch 68/100

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39/39 [==============================] - 1s 38ms/step - loss: 0.9297 - accuracy: 0.8760

39/39 [==============================] - 2s 45ms/step - loss: 0.9297 - accuracy: 0.8760 - val_loss: 1.3763 - val_accuracy: 0.7708
Epoch 69/100

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39/39 [==============================] - 1s 38ms/step - loss: 0.9233 - accuracy: 0.8802

39/39 [==============================] - 2s 47ms/step - loss: 0.9233 - accuracy: 0.8802 - val_loss: 1.3756 - val_accuracy: 0.7792
Epoch 70/100

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39/39 [==============================] - 1s 36ms/step - loss: 0.9167 - accuracy: 0.8813

39/39 [==============================] - 2s 43ms/step - loss: 0.9167 - accuracy: 0.8813 - val_loss: 1.3680 - val_accuracy: 0.7479
Epoch 71/100

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39/39 [==============================] - 2s 39ms/step - loss: 0.9243 - accuracy: 0.8755

39/39 [==============================] - 2s 48ms/step - loss: 0.9243 - accuracy: 0.8755 - val_loss: 1.3754 - val_accuracy: 0.7750
Epoch 72/100

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39/39 [==============================] - 1s 37ms/step - loss: 0.8918 - accuracy: 0.8906

39/39 [==============================] - 2s 43ms/step - loss: 0.8918 - accuracy: 0.8906 - val_loss: 1.4000 - val_accuracy: 0.7812
Epoch 73/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.8726 - accuracy: 0.8958

39/39 [==============================] - 2s 49ms/step - loss: 0.8726 - accuracy: 0.8958 - val_loss: 1.4653 - val_accuracy: 0.7771
Epoch 74/100

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39/39 [==============================] - 1s 35ms/step - loss: 0.8496 - accuracy: 0.8964

39/39 [==============================] - 2s 44ms/step - loss: 0.8496 - accuracy: 0.8964 - val_loss: 1.4708 - val_accuracy: 0.7771
Epoch 75/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.8483 - accuracy: 0.9000

39/39 [==============================] - 2s 47ms/step - loss: 0.8483 - accuracy: 0.9000 - val_loss: 1.4772 - val_accuracy: 0.7979
Epoch 76/100

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39/39 [==============================] - 1s 37ms/step - loss: 0.8450 - accuracy: 0.8995

39/39 [==============================] - 2s 45ms/step - loss: 0.8450 - accuracy: 0.8995 - val_loss: 1.4168 - val_accuracy: 0.7917
Epoch 77/100

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39/39 [==============================] - 2s 39ms/step - loss: 0.8703 - accuracy: 0.8911

39/39 [==============================] - 2s 45ms/step - loss: 0.8703 - accuracy: 0.8911 - val_loss: 1.5344 - val_accuracy: 0.7875
Epoch 78/100

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39/39 [==============================] - 1s 38ms/step - loss: 0.8738 - accuracy: 0.8906

39/39 [==============================] - 2s 46ms/step - loss: 0.8738 - accuracy: 0.8906 - val_loss: 1.4429 - val_accuracy: 0.7854
Epoch 79/100

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39/39 [==============================] - 1s 37ms/step - loss: 0.8732 - accuracy: 0.8911

39/39 [==============================] - 2s 43ms/step - loss: 0.8732 - accuracy: 0.8911 - val_loss: 1.3851 - val_accuracy: 0.7625
Epoch 80/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.8375 - accuracy: 0.8953

39/39 [==============================] - 2s 48ms/step - loss: 0.8375 - accuracy: 0.8953 - val_loss: 1.3983 - val_accuracy: 0.7667
Epoch 81/100

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39/39 [==============================] - 1s 35ms/step - loss: 0.8487 - accuracy: 0.8849

39/39 [==============================] - 2s 42ms/step - loss: 0.8487 - accuracy: 0.8849 - val_loss: 1.5364 - val_accuracy: 0.8208
Epoch 82/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.8272 - accuracy: 0.8953

39/39 [==============================] - 2s 49ms/step - loss: 0.8272 - accuracy: 0.8953 - val_loss: 1.4269 - val_accuracy: 0.7625
Epoch 83/100

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39/39 [==============================] - 1s 34ms/step - loss: 0.8170 - accuracy: 0.8969

39/39 [==============================] - 2s 43ms/step - loss: 0.8170 - accuracy: 0.8969 - val_loss: 1.4062 - val_accuracy: 0.7812
Epoch 84/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.8122 - accuracy: 0.8979

39/39 [==============================] - 2s 47ms/step - loss: 0.8122 - accuracy: 0.8979 - val_loss: 1.3602 - val_accuracy: 0.7771
Epoch 85/100

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39/39 [==============================] - 1s 36ms/step - loss: 0.8168 - accuracy: 0.8901

39/39 [==============================] - 2s 45ms/step - loss: 0.8168 - accuracy: 0.8901 - val_loss: 1.4381 - val_accuracy: 0.7854
Epoch 86/100

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39/39 [==============================] - 1s 37ms/step - loss: 0.7839 - accuracy: 0.9026

39/39 [==============================] - 2s 44ms/step - loss: 0.7839 - accuracy: 0.9026 - val_loss: 1.4611 - val_accuracy: 0.8021
Epoch 87/100

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39/39 [==============================] - 2s 39ms/step - loss: 0.7740 - accuracy: 0.9219

39/39 [==============================] - 2s 48ms/step - loss: 0.7740 - accuracy: 0.9219 - val_loss: 1.3854 - val_accuracy: 0.7750
Epoch 88/100

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39/39 [==============================] - 1s 35ms/step - loss: 0.7590 - accuracy: 0.9177

39/39 [==============================] - 2s 43ms/step - loss: 0.7590 - accuracy: 0.9177 - val_loss: 1.4130 - val_accuracy: 0.7792
Epoch 89/100

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39/39 [==============================] - 2s 40ms/step - loss: 0.7534 - accuracy: 0.9146

39/39 [==============================] - 2s 48ms/step - loss: 0.7534 - accuracy: 0.9146 - val_loss: 1.4488 - val_accuracy: 0.7917
Epoch 90/100

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39/39 [==============================] - 1s 36ms/step - loss: 0.7390 - accuracy: 0.9083

39/39 [==============================] - 2s 44ms/step - loss: 0.7390 - accuracy: 0.9083 - val_loss: 1.4028 - val_accuracy: 0.7896
Epoch 91/100

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39/39 [==============================] - 1s 37ms/step - loss: 0.7485 - accuracy: 0.9089

39/39 [==============================] - 2s 43ms/step - loss: 0.7485 - accuracy: 0.9089 - val_loss: 1.4807 - val_accuracy: 0.8000
Epoch 92/100

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39/39 [==============================] - 2s 39ms/step - loss: 0.7523 - accuracy: 0.9057

39/39 [==============================] - 2s 47ms/step - loss: 0.7523 - accuracy: 0.9057 - val_loss: 1.3704 - val_accuracy: 0.8042
Epoch 93/100

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39/39 [==============================] - 2s 44ms/step - loss: 0.7327 - accuracy: 0.9094 - val_loss: 1.3603 - val_accuracy: 0.7896
Epoch 94/100

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39/39 [==============================] - 2s 39ms/step - loss: 0.7348 - accuracy: 0.9161

39/39 [==============================] - 2s 46ms/step - loss: 0.7348 - accuracy: 0.9161 - val_loss: 1.4322 - val_accuracy: 0.8021
Epoch 95/100

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39/39 [==============================] - 2s 45ms/step - loss: 0.7353 - accuracy: 0.9125 - val_loss: 1.3779 - val_accuracy: 0.8000
Epoch 96/100

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39/39 [==============================] - 2s 43ms/step - loss: 0.7391 - accuracy: 0.9109 - val_loss: 1.4209 - val_accuracy: 0.7979
Epoch 97/100

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39/39 [==============================] - 2s 48ms/step - loss: 0.7098 - accuracy: 0.9146 - val_loss: 1.3965 - val_accuracy: 0.7833
Epoch 98/100

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39/39 [==============================] - 2s 43ms/step - loss: 0.7227 - accuracy: 0.8995 - val_loss: 1.3536 - val_accuracy: 0.7708
Epoch 99/100

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39/39 [==============================] - 2s 46ms/step - loss: 0.7077 - accuracy: 0.9073 - val_loss: 1.3884 - val_accuracy: 0.7896
Epoch 100/100

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39/39 [==============================] - 2s 44ms/step - loss: 0.6793 - accuracy: 0.9198 - val_loss: 1.4331 - val_accuracy: 0.8250

Error in WeightedROC(prob_pred, label_test, weight_test) : 
  length(label) == length(guess) is not TRUE

Visualize cross-validation results.

res_cv_dnn <- as.data.frame(res_cv_dnn)

if(run.cv_dnn){
  p1 <- res_cv_dnn %>%
    ggplot(aes(x = lrs, y = mean_error,
               ymin = mean_error - sd_error, ymax = mean_error + sd_error)) +
    geom_crossbar() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  p2 <- res_cv_dnn %>%
    ggplot(aes(x = lrs, y = mean_AUC,
               ymin = mean_AUC - sd_AUC, ymax = mean_AUC + sd_AUC)) +
    geom_crossbar() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1))
  
  print(p1)
  print(p2)
}

• Choose the “best” parameter value

# par_lr_best <- as.numeric(res_cv_dnn[which.min(res_cv_dnn$mean_error), 1])
par_lr_best = 0.0002

• Train the model with the entire training set using the selected model (model parameter) via cross-validation.

source("../lib/train_dnn.R") 
source("../lib/test_dnn.R")

feature_train = as.matrix(dat_train[, -6007])
label_train = as.integer(dat_train$label)
feature_test <- as.matrix(dat_test[, -6007])
label_test <- as.integer(dat_test$label)
label_test <- ifelse(label_test == 2, 1, 0)
weight_test <- rep(NA, length(label_test))

# training weights
weight_train <- rep(NA, length(label_train))
for (v in unique(label_train)){
  weight_train[label_train == v] = 0.5 * length(label_train) / length(label_train[label_train == v])
}

for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

tm_train <- NA
if (sample.reweight){
  tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = weight_train, feature_test, label_test, weight_test, par_lr_best))
} else {
  tm_train <- system.time(fit_train <- train_dnn(feature_train, label_train, w = NULL, feature_test, label_test, weight_test = NULL, par_lr_best))
}
save_model_hdf5(fit_train, "../output/fit_train_dnn.h5")

Step 5: Run test on test images

tm_test = NA
feature_test <- as.matrix(dat_test[, -6007])
if(run.test_dnn){
  fit_train <- load_model_hdf5("../output/fit_train_dnn.h5")
  tm_test <- system.time({label_pred <- test_dnn(fit_train, feature_test, type = "predict_classes");})
  prob_pred <- test_dnn(fit_train, feature_test, type = "predict_proba")
}

• evaluation

## reweight the test data to represent a balanced label distribution
label_test <- as.integer(dat_test$label)

weight_test <- rep(NA, length(label_test))
for (v in unique(label_test)){
  weight_test[label_test == v] = 0.5 * length(label_test) / length(label_test[label_test == v])
}

label_test <- ifelse(label_test == 2, 1, 0)

accu <- sum(weight_test * (label_pred == label_test)) / sum(weight_test)

# prob_pred <- apply(prob_pred, 1, max)
prob_pred <- prob_pred[, 2]

tpr.fpr <- WeightedROC(prob_pred, label_test, weight_test)
auc <- WeightedAUC(tpr.fpr)


cat("The accuracy of model:", "DNN with lr =", par_lr_best, "is", accu*100, "%.\n")
cat("The AUC of model:", "DNN", "is", auc, ".\n")

Summarize Running Time

Prediction performance matters, so does the running times for constructing features and for training the model, especially when the computation resource is limited.

cat("Time for constructing training features=", tm_feature_train[3], "s \n")
cat("Time for constructing testing features=", tm_feature_test[3], "s \n")
cat("Time for training model=", tm_train[3], "s \n") 
cat("Time for testing model=", tm_test[3], "s \n")

###Reference - Du, S., Tao, Y., & Martinez, A. M. (2014). Compound facial expressions of emotion. Proceedings of the National Academy of Sciences, 111(15), E1454-E1462.